NMDS Reference Bugs- Spring and Fall separated
18 August, 2021
R version 4.0.3 (2020-10-10) – “Bunny-Wunnies Freak Out”
Packages used for NMDS: vegan (version 2.5-7)
Methods
These NMDS results will support the Genus level IBI development process. No West Virginia DEP data is used in this analysis. The ordinations presented in this document were separated by spring and fall and NMDS was run separately. The natural characteristics displayed in the following figures are characteristics identified as potentially having different benthic communities when separated by season. In previous iterations of these ordinations, the expert group identified that season clustered into two separate groups, spring and fall. This means that the benthic communities are possibly different between spring and fall communities.
The dataset used is a subset of reference stations collected in Virginia and only includes noncoastal sites that are not located in the Southeastern Plains or MidAtlantic Coastal. If stations appeared in the dataset more than 4 times, then the most recent 4 samples were used and the rest removed. Taxa that occurred in the dataset < 5% of the time were removed. The data was log10 +1 transformed. Environmental factors were compiled for each station and used to plot over the NMDS to show environmental variation associated with the community matrix. The envfit function in Vegan was used to plot the continuous environmental variables.
The first step was to read in the reference site bug taxa list and environmental factors dataset for each station. Join the environmental dataset with the bug dataset to account for multiple observations of each station and collection date and time.
Check to make sure the bug and environmental join was successful:
Number of rows in Spring Community Matrix: 338
Number or rows in Spring Environmental Matrix: 339
Number of rows in Fall Community Matrix: 346
Number or rows in Fall Environmental Matrix: 347
The data was log10+1 transformed. Rare (<=5%) taxa were removed.
Run NMDS for Spring reference communities
## Run 0 stress 0.1754646
## Run 1 stress 0.176773
## Run 2 stress 0.1754666
## ... Procrustes: rmse 0.0008463922 max resid 0.01479136
## Run 3 stress 0.1755717
## ... Procrustes: rmse 0.003806968 max resid 0.06855528
## Run 4 stress 0.1754667
## ... Procrustes: rmse 0.000839037 max resid 0.0146182
## Run 5 stress 0.1755716
## ... Procrustes: rmse 0.003799241 max resid 0.06843972
## Run 6 stress 0.175468
## ... Procrustes: rmse 0.000963138 max resid 0.01525071
## Run 7 stress 0.1766921
## Run 8 stress 0.1755715
## ... Procrustes: rmse 0.003801827 max resid 0.06847634
## Run 9 stress 0.1761763
## Run 10 stress 0.1755715
## ... Procrustes: rmse 0.003792908 max resid 0.0684244
## Run 11 stress 0.1754644
## ... New best solution
## ... Procrustes: rmse 8.366446e-05 max resid 0.001098954
## ... Similar to previous best
## Run 12 stress 0.1763459
## Run 13 stress 0.1764369
## Run 14 stress 0.1763448
## Run 15 stress 0.1755716
## ... Procrustes: rmse 0.003775026 max resid 0.0680691
## Run 16 stress 0.1763454
## Run 17 stress 0.1766923
## Run 18 stress 0.1755466
## ... Procrustes: rmse 0.00363668 max resid 0.0623399
## Run 19 stress 0.1766926
## Run 20 stress 0.1756266
## ... Procrustes: rmse 0.005278839 max resid 0.06861148
## *** Solution reached##
## Call:
## metaMDS(comm = SpringNMDSFive[, 6:100], k = 3, trymax = 1000)
##
## global Multidimensional Scaling using monoMDS
##
## Data: SpringNMDSFive[, 6:100]
## Distance: bray
##
## Dimensions: 3
## Stress: 0.1754644
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'SpringNMDSFive[, 6:100]'
## NMDS1 NMDS2 r2 Pr(>r)
## Year -0.90611 0.42305 0.0447 0.10 .
## JulianDate 0.45850 0.88869 0.4934 0.01 **
## Latitude -0.03938 -0.99922 0.1471 0.01 **
## Longitude 0.03557 -0.99937 0.2242 0.01 **
## totalArea_sqMile 0.88411 -0.46729 0.4554 0.01 **
## ELEVMEAN -0.74454 0.66757 0.2227 0.01 **
## SLPMEAN -0.80365 0.59510 0.1664 0.01 **
## wshdRain_mmyr 0.89716 -0.44171 0.4320 0.01 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 99NMDS for Fall communities
## Run 0 stress 0.1767604
## Run 1 stress 0.1777459
## Run 2 stress 0.1766652
## ... New best solution
## ... Procrustes: rmse 0.01350834 max resid 0.1066939
## Run 3 stress 0.1784162
## Run 4 stress 0.177231
## Run 5 stress 0.1777303
## Run 6 stress 0.1768955
## ... Procrustes: rmse 0.005893447 max resid 0.07716706
## Run 7 stress 0.1765769
## ... New best solution
## ... Procrustes: rmse 0.005649696 max resid 0.09384299
## Run 8 stress 0.1767772
## ... Procrustes: rmse 0.006795274 max resid 0.0941703
## Run 9 stress 0.1798834
## Run 10 stress 0.1769686
## ... Procrustes: rmse 0.0114333 max resid 0.1050683
## Run 11 stress 0.1765734
## ... New best solution
## ... Procrustes: rmse 0.000456017 max resid 0.005679855
## ... Similar to previous best
## Run 12 stress 0.1777172
## Run 13 stress 0.1776926
## Run 14 stress 0.1767348
## ... Procrustes: rmse 0.01031192 max resid 0.1032042
## Run 15 stress 0.1785295
## Run 16 stress 0.1768108
## ... Procrustes: rmse 0.01270484 max resid 0.1059158
## Run 17 stress 0.1784291
## Run 18 stress 0.1765847
## ... Procrustes: rmse 0.003462091 max resid 0.05323781
## Run 19 stress 0.1768456
## ... Procrustes: rmse 0.004519837 max resid 0.05524578
## Run 20 stress 0.176946
## ... Procrustes: rmse 0.009587787 max resid 0.102346
## *** Solution reached##
## Call:
## metaMDS(comm = FallNMDSFive[, 6:107], k = 3, trymax = 1000)
##
## global Multidimensional Scaling using monoMDS
##
## Data: FallNMDSFive[, 6:107]
## Distance: bray
##
## Dimensions: 3
## Stress: 0.1765734
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'FallNMDSFive[, 6:107]'
Spring plot with Station IDs
Plot with Axis 3- Spring
Fall plot with Station IDs
Plot with Axis 3- Fall
Plot with Species-Spring
Fall plot with species
Ecoregion- Spring
##
## Call:
## mrpp(dat = bugsnms_Spring[, 6:100], grouping = samplescoresenv_Spring$US_L3NAME, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Blue Ridge Central Appalachians Northern Piedmont Piedmont
## delta 0.5389 0.5493 0.6053 0.5876
## n 78 18 50 67
## Ridge and Valley
## delta 0.6228
## n 125
##
## Chance corrected within-group agreement A: 0.04605
## Based on observed delta 0.5899 and expected delta 0.6184
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999Ecoregion-Fall
##
## Call:
## mrpp(dat = bugsnms_Fall[, 6:107], grouping = samplescoresenv_Fall$US_L3NAME, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Blue Ridge Central Appalachians Northern Piedmont Piedmont
## delta 0.5847 0.59 0.5996 0.5688
## n 76 22 51 70
## Ridge and Valley
## delta 0.6217
## n 127
##
## Chance corrected within-group agreement A: 0.04921
## Based on observed delta 0.5976 and expected delta 0.6286
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999##Bioregion-Spring
##
## Call:
## mrpp(dat = bugsnms_Spring[, 6:100], grouping = samplescoresenv_Spring$Bioregion, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Mountain Piedmont
## delta 0.6048 0.6089
## n 221 117
##
## Chance corrected within-group agreement A: 0.01973
## Based on observed delta 0.6062 and expected delta 0.6184
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999Bioregion- Fall
##
## Call:
## mrpp(dat = bugsnms_Fall[, 6:107], grouping = samplescoresenv_Fall$Bioregion, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Mountain Piedmont
## delta 0.625 0.5935
## n 225 121
##
## Chance corrected within-group agreement A: 0.02313
## Based on observed delta 0.614 and expected delta 0.6286
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999Stream Order- Spring
##
## Call:
## mrpp(dat = bugsnms_Spring[, 6:100], grouping = samplescoresenv_Spring$Order, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## 1 2 3 4 5
## delta 0.5782 0.6129 0.5864 0.5678 0.522
## n 125 97 68 37 11
##
## Chance corrected within-group agreement A: 0.05102
## Based on observed delta 0.5869 and expected delta 0.6184
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999Stream Order- Fall
##
## Call:
## mrpp(dat = bugsnms_Fall[, 6:107], grouping = samplescoresenv_Fall$Order, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## 1 2 3 4 5
## delta 0.5978 0.6326 0.5863 0.5221 0.4953
## n 118 108 69 37 14
##
## Chance corrected within-group agreement A: 0.05479
## Based on observed delta 0.5941 and expected delta 0.6286
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999Stream Order Categories- Spring
##
## Call:
## mrpp(dat = bugsnms_Spring[, 6:100], grouping = samplescoresenv_Spring$StreamCate, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Large Small
## delta 0.5847 0.5995
## n 116 222
##
## Chance corrected within-group agreement A: 0.03881
## Based on observed delta 0.5944 and expected delta 0.6184
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999Stream Order Categories- Fall
##
## Call:
## mrpp(dat = bugsnms_Fall[, 6:107], grouping = samplescoresenv_Fall$StreamCate, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Large Small
## delta 0.568 0.6245
## n 120 226
##
## Chance corrected within-group agreement A: 0.03758
## Based on observed delta 0.6049 and expected delta 0.6286
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999Bioregion and Size- Spring
##
## Call:
## mrpp(dat = bugsnms_Spring[, 6:100], grouping = samplescoresenv_Spring$BioregionSize, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## MountainLarge MountainSmall PiedmontLarge PiedmontSmall
## delta 0.5666 0.5777 0.5824 0.591
## n 77 144 39 78
##
## Chance corrected within-group agreement A: 0.0641
## Based on observed delta 0.5788 and expected delta 0.6184
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999Bioregion and Size- Fall
##
## Call:
## mrpp(dat = bugsnms_Fall[, 6:107], grouping = samplescoresenv_Fall$BioregionSize, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## MountainLarge MountainSmall PiedmontLarge PiedmontSmall
## delta 0.5564 0.6126 0.5473 0.5878
## n 80 145 40 81
##
## Chance corrected within-group agreement A: 0.0673
## Based on observed delta 0.5863 and expected delta 0.6286
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999